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A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks

Accepted at LHMP2020 Workshop (ICRA 2020)
 
: Hug, Ronny; Hübner, Wolfgang; Arens, Michael

:
Fulltext urn:nbn:de:0011-n-5959215 (1.9 MByte PDF)
MD5 Fingerprint: e21bd15695befd004b760549f2b741f6
Created on: 21.7.2020


Online im WWW, 2020, 2 pp.
International Conference on Robotics and Automation (ICRA) <2020, online>
Workshop on Long-term Human Motion Prediction (LHMP) <2, 2020, Online>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Abstract
The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks. In order to enable a more informative assembly of a data basis, an approach for determining a dataset representation in terms of a small set of distinguishable prototypical sub-sequences is proposed. The approach employs a sequence alignment followed by a learning vector quantization (LVQ) stage. A first proof of concept on synthetically generated and real-world datasets shows the viability of the approach.

: http://publica.fraunhofer.de/documents/N-595921.html